
"""simple docstring"""






import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCAmelCase        =				{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}




def __magic_name__	(				_lowerCamelCase:		Optional[int]			)					->						Any:

	'''simple docstring'''




	assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
	assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
	if args.mlm:
		assert os.path.isfile(args.token_counts			)
		assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
	else:
		assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

	assert args.teacher_type == args.student_type or (
	    args.student_type == "distilbert" and args.teacher_type == "bert"
	)
	assert os.path.isfile(args.student_config			)
	if args.student_pretrained_weights is not None:
		assert os.path.isfile(args.student_pretrained_weights			)

	if args.freeze_token_type_embds:
		assert args.student_type in ["roberta"]

	assert args.alpha_ce >= 0.0
	assert args.alpha_mlm >= 0.0
	assert args.alpha_clm >= 0.0
	assert args.alpha_mse >= 0.0
	assert args.alpha_cos >= 0.0
	assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0




def __magic_name__	(				_lowerCamelCase:		Tuple,					_lowerCamelCase:		List[str]			)					->						Union[str, Any]:

	'''simple docstring'''




	if args.student_type == "roberta":
		lowerCAmelCase												=  False
	elif args.student_type == "gpt2":
		lowerCAmelCase												=  False




def __magic_name__	(				_lowerCamelCase:		Optional[int],					_lowerCamelCase:		List[Any]			)					->						Optional[Any]:

	'''simple docstring'''




	if args.student_type == "roberta":
		lowerCAmelCase												=  False




def __magic_name__	(				)					->						Union[str, Any]:

	'''simple docstring'''




	lowerCAmelCase												=  argparse.ArgumentParser(description='''Training'''			)
	parser.add_argument('''--force''',					action='''store_true''',					help='''Overwrite dump_path if it already exists.'''			)

	parser.add_argument(
	    '''--dump_path''',					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''The output directory (log, checkpoints, parameters, etc.)'''			)
	parser.add_argument(
	    '''--data_file''',					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''The binarized file (tokenized + tokens_to_ids) and grouped by sequence.''',					)

	parser.add_argument(
	    '''--student_type''',					type=_lowerCamelCase,					choices=['''distilbert''', '''roberta''', '''gpt2'''],					required=_lowerCamelCase,					help='''The student type (DistilBERT, RoBERTa).''',					)
	parser.add_argument('''--student_config''',					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''Path to the student configuration.'''			)
	parser.add_argument(
	    '''--student_pretrained_weights''',					default=_lowerCamelCase,					type=_lowerCamelCase,					help='''Load student initialization checkpoint.'''			)

	parser.add_argument(
	    '''--teacher_type''',					choices=['''bert''', '''roberta''', '''gpt2'''],					required=_lowerCamelCase,					help='''Teacher type (BERT, RoBERTa).'''			)
	parser.add_argument('''--teacher_name''',					type=_lowerCamelCase,					required=_lowerCamelCase,					help='''The teacher model.'''			)

	parser.add_argument('''--temperature''',					default=2.0,					type=_lowerCamelCase,					help='''Temperature for the softmax temperature.'''			)
	parser.add_argument(
	    '''--alpha_ce''',					default=0.5,					type=_lowerCamelCase,					help='''Linear weight for the distillation loss. Must be >=0.'''			)
	parser.add_argument(
	    '''--alpha_mlm''',					default=0.0,					type=_lowerCamelCase,					help='''Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.''',					)
	parser.add_argument('''--alpha_clm''',					default=0.5,					type=_lowerCamelCase,					help='''Linear weight for the CLM loss. Must be >=0.'''			)
	parser.add_argument('''--alpha_mse''',					default=0.0,					type=_lowerCamelCase,					help='''Linear weight of the MSE loss. Must be >=0.'''			)
	parser.add_argument(
	    '''--alpha_cos''',					default=0.0,					type=_lowerCamelCase,					help='''Linear weight of the cosine embedding loss. Must be >=0.'''			)

	parser.add_argument(
	    '''--mlm''',					action='''store_true''',					help='''The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'''			)
	parser.add_argument(
	    '''--mlm_mask_prop''',					default=0.15,					type=_lowerCamelCase,					help='''Proportion of tokens for which we need to make a prediction.''',					)
	parser.add_argument('''--word_mask''',					default=0.8,					type=_lowerCamelCase,					help='''Proportion of tokens to mask out.'''			)
	parser.add_argument('''--word_keep''',					default=0.1,					type=_lowerCamelCase,					help='''Proportion of tokens to keep.'''			)
	parser.add_argument('''--word_rand''',					default=0.1,					type=_lowerCamelCase,					help='''Proportion of tokens to randomly replace.'''			)
	parser.add_argument(
	    '''--mlm_smoothing''',					default=0.7,					type=_lowerCamelCase,					help='''Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).''',					)
	parser.add_argument('''--token_counts''',					type=_lowerCamelCase,					help='''The token counts in the data_file for MLM.'''			)

	parser.add_argument(
	    '''--restrict_ce_to_mask''',					action='''store_true''',					help='''If true, compute the distillation loss only the [MLM] prediction distribution.''',					)
	parser.add_argument(
	    '''--freeze_pos_embs''',					action='''store_true''',					help='''Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.''',					)
	parser.add_argument(
	    '''--freeze_token_type_embds''',					action='''store_true''',					help='''Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.''',					)

	parser.add_argument('''--n_epoch''',					type=_lowerCamelCase,					default=3,					help='''Number of pass on the whole dataset.'''			)
	parser.add_argument('''--batch_size''',					type=_lowerCamelCase,					default=5,					help='''Batch size (for each process).'''			)
	parser.add_argument(
	    '''--group_by_size''',					action='''store_false''',					help='''If true, group sequences that have similar length into the same batch. Default is true.''',					)

	parser.add_argument(
	    '''--gradient_accumulation_steps''',					type=_lowerCamelCase,					default=50,					help='''Gradient accumulation for larger training batches.''',					)
	parser.add_argument('''--warmup_prop''',					default=0.05,					type=_lowerCamelCase,					help='''Linear warmup proportion.'''			)
	parser.add_argument('''--weight_decay''',					default=0.0,					type=_lowerCamelCase,					help='''Weight decay if we apply some.'''			)
	parser.add_argument('''--learning_rate''',					default=5E-4,					type=_lowerCamelCase,					help='''The initial learning rate for Adam.'''			)
	parser.add_argument('''--adam_epsilon''',					default=1E-6,					type=_lowerCamelCase,					help='''Epsilon for Adam optimizer.'''			)
	parser.add_argument('''--max_grad_norm''',					default=5.0,					type=_lowerCamelCase,					help='''Max gradient norm.'''			)
	parser.add_argument('''--initializer_range''',					default=0.02,					type=_lowerCamelCase,					help='''Random initialization range.'''			)

	parser.add_argument(
	    '''--fp16''',					action='''store_true''',					help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit''',					)
	parser.add_argument(
	    '''--fp16_opt_level''',					type=_lowerCamelCase,					default='''O1''',					help=(
	        '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'''
	        '''See details at https://nvidia.github.io/apex/amp.html'''
	    ),					)
	parser.add_argument('''--n_gpu''',					type=_lowerCamelCase,					default=1,					help='''Number of GPUs in the node.'''			)
	parser.add_argument('''--local_rank''',					type=_lowerCamelCase,					default=-1,					help='''Distributed training - Local rank'''			)
	parser.add_argument('''--seed''',					type=_lowerCamelCase,					default=56,					help='''Random seed'''			)

	parser.add_argument('''--log_interval''',					type=_lowerCamelCase,					default=500,					help='''Tensorboard logging interval.'''			)
	parser.add_argument('''--checkpoint_interval''',					type=_lowerCamelCase,					default=4_000,					help='''Checkpoint interval.'''			)
	lowerCAmelCase												=  parser.parse_args()
	sanity_checks(_lowerCamelCase			)

	# ARGS #
	init_gpu_params(_lowerCamelCase			)
	set_seed(_lowerCamelCase			)
	if args.is_master:
		if os.path.exists(args.dump_path			):
			if not args.force:
				raise ValueError(
				    F"""Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite"""
				    ''' itUse `--force` if you want to overwrite it'''			)
			else:
				shutil.rmtree(args.dump_path			)

		if not os.path.exists(args.dump_path			):
			os.makedirs(args.dump_path			)
		logger.info(F"""Experiment will be dumped and logged in {args.dump_path}"""			)

		# SAVE PARAMS #
		logger.info(F"""Param: {args}"""			)
		with open(os.path.join(args.dump_path,					'''parameters.json'''			),					'''w'''			) as f:
			json.dump(vars(_lowerCamelCase			),					_lowerCamelCase,					indent=4			)
		git_log(args.dump_path			)

	lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  MODEL_CLASSES[args.student_type]
	lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  MODEL_CLASSES[args.teacher_type]

	# TOKENIZER #
	lowerCAmelCase												=  teacher_tokenizer_class.from_pretrained(args.teacher_name			)
	lowerCAmelCase												=  {}
	for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
		lowerCAmelCase												=  tokenizer.all_special_tokens.index(_lowerCamelCase			)
		lowerCAmelCase												=  tokenizer.all_special_ids[idx]
	logger.info(F"""Special tokens {special_tok_ids}"""			)
	lowerCAmelCase												=  special_tok_ids
	lowerCAmelCase												=  tokenizer.max_model_input_sizes[args.teacher_name]

	# DATA LOADER #
	logger.info(F"""Loading data from {args.data_file}"""			)
	with open(args.data_file,					'''rb'''			) as fp:
		lowerCAmelCase												=  pickle.load(_lowerCamelCase			)

	if args.mlm:
		logger.info(F"""Loading token counts from {args.token_counts} (already pre-computed)"""			)
		with open(args.token_counts,					'''rb'''			) as fp:
			lowerCAmelCase												=  pickle.load(_lowerCamelCase			)

		lowerCAmelCase												=  np.maximum(_lowerCamelCase,					1			) ** -args.mlm_smoothing
		for idx in special_tok_ids.values():
			lowerCAmelCase												=  0.0  # do not predict special tokens
		lowerCAmelCase												=  torch.from_numpy(_lowerCamelCase			)
	else:
		lowerCAmelCase												=  None

	lowerCAmelCase												=  LmSeqsDataset(params=_lowerCamelCase,					data=_lowerCamelCase			)
	logger.info('''Data loader created.'''			)

	# STUDENT #
	logger.info(F"""Loading student config from {args.student_config}"""			)
	lowerCAmelCase												=  student_config_class.from_pretrained(args.student_config			)
	lowerCAmelCase												=  True

	if args.student_pretrained_weights is not None:
		logger.info(F"""Loading pretrained weights from {args.student_pretrained_weights}"""			)
		lowerCAmelCase												=  student_model_class.from_pretrained(args.student_pretrained_weights,					config=_lowerCamelCase			)
	else:
		lowerCAmelCase												=  student_model_class(_lowerCamelCase			)

	if args.n_gpu > 0:
		student.to(F"""cuda:{args.local_rank}"""			)
	logger.info('''Student loaded.'''			)

	# TEACHER #
	lowerCAmelCase												=  teacher_model_class.from_pretrained(args.teacher_name,					output_hidden_states=_lowerCamelCase			)
	if args.n_gpu > 0:
		teacher.to(F"""cuda:{args.local_rank}"""			)
	logger.info(F"""Teacher loaded from {args.teacher_name}."""			)

	# FREEZING #
	if args.freeze_pos_embs:
		freeze_pos_embeddings(_lowerCamelCase,					_lowerCamelCase			)
	if args.freeze_token_type_embds:
		freeze_token_type_embeddings(_lowerCamelCase,					_lowerCamelCase			)

	# SANITY CHECKS #
	assert student.config.vocab_size == teacher.config.vocab_size
	assert student.config.hidden_size == teacher.config.hidden_size
	assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
	if args.mlm:
		assert token_probs.size(0			) == stu_architecture_config.vocab_size

	# DISTILLER #
	torch.cuda.empty_cache()
	lowerCAmelCase												=  Distiller(
	    params=_lowerCamelCase,					dataset=_lowerCamelCase,					token_probs=_lowerCamelCase,					student=_lowerCamelCase,					teacher=_lowerCamelCase			)
	distiller.train()
	logger.info('''Let\'s go get some drinks.'''			)


if __name__ == "__main__":
					main()




"""simple docstring"""






import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar



class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_logger()

				# the current default level is logging.WARNING
				lowerCAmelCase												=  logging.get_verbosity()

				logging.set_verbosity_error()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_warning()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_info()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				logging.set_verbosity_debug()
				self.assertEqual(logger.getEffectiveLevel()      ,logging.get_verbosity()			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  logging.get_verbosity()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
				if level_origin <= logging.WARNING:
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)

				# this is setting the level for all of `transformers.*` loggers
				logging.set_verbosity_error()

				# should not be able to log warnings
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,''''''			)

				# should be able to log warnings again
				logging.set_verbosity_warning()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					logger.warning(SCREAMING_SNAKE_CASE_			)
				self.assertEqual(cl.out      ,msg + '''\n'''			)

				# restore to the original level
				logging.set_verbosity(SCREAMING_SNAKE_CASE_			)






			@mockenv(TRANSFORMERS_VERBOSITY='''error'''			)
			def 					UpperCAmelCase							(self  :	Any			)       ->	str:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				# this action activates the env var
				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)

				lowerCAmelCase												=  os.getenv('''TRANSFORMERS_VERBOSITY'''      ,SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  logging.log_levels[env_level_str]

				lowerCAmelCase												=  logging.get_verbosity()
				self.assertEqual(
				    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""      ,)

				# restore to the original level
				lowerCAmelCase												=  ''''''
				transformers.utils.logging._reset_library_root_logger()






			@mockenv(TRANSFORMERS_VERBOSITY='''super-error'''			)
			def 					UpperCAmelCase							(self  :	List[Any]			)       ->	Tuple:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()
				lowerCAmelCase												=  logging.logging.getLogger()
				with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
					# this action activates the env var
					logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''      ,cl.out			)

				# no need to restore as nothing was changed






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Any:





				"""simple docstring"""


				transformers.utils.logging._reset_library_root_logger()

				lowerCAmelCase												=  logging.get_logger('''transformers.models.bart.tokenization_bart'''			)
				lowerCAmelCase												=  '''Testing 1, 2, 3'''

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''			):
					# nothing should be logged as env var disables this method
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,''''''			)

				with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''			):
					# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
					with CaptureLogger(SCREAMING_SNAKE_CASE_			) as cl:
						logger.warning_advice(SCREAMING_SNAKE_CASE_			)
					self.assertEqual(cl.out      ,msg + '''\n'''			)





def __magic_name__	(				)					->						List[Any]:

	'''simple docstring'''




	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()


